Project description:This is the experiment set used for a paper written in collaboration with Hopkins. It compares genes that are differentially expressed between normal pancreas, pancreatic cell lines and pancreatic adenocarcinoma. Pancreatic cancer is the fifth leading cause of cancer death in the United States. We used cDNA microarrays to analyze global gene expression patterns in 14 pancreatic cancer cell lines, 17 resected infiltrating pancreatic cancer tissues, and 5 samples of normal pancreas to identify genes that are differentially expressed in pancreatic cancer. We found more than 400 cDNAs corresponding to genes that were differentially expressed in the pancreatic cancer tissues and cell lines as compared to normal pancreas. These genes that tended to be expressed at higher levels in pancreatic cancers were associated with a variety of processes, including cell-cell and cell-matrix interactions, cytoskeletal remodeling, proteolytic activity, and Ca(++) homeostasis. Two prominent clusters of genes were related to the high rates of cellular proliferation in pancreatic cancer cell lines and the host desmoplastic response in the resected pancreatic cancer tissues. Of 149 genes identified as more highly expressed in the pancreatic cancers compared with normal pancreas, 103 genes have not been previously reported in association with pancreatic cancer. The expression patterns of 14 of these highly expressed genes were validated by either immunohistochemistry or reverse transcriptase-polymerase chain reaction as being expressed in pancreatic cancer. The overexpression of one gene in particular, 14-3-3 sigma, was found to be associated with aberrant hypomethylation in the majority of pancreatic cancers analyzed. The genes and expressed sequence tags presented in this study provide clues to the pathobiology of pancreatic cancer and implicate a large number of potentially new molecular markers for the detection and treatment of pancreatic cancer. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Using regression correlation
Project description:Human plasma proteome profiling of pancreatic cancer patients and non-disease healthy controls. EVs isolated (Nova), characterized, and DIA-based proteomics performed. Comparative analyses on oncofactors in EVs, Pancreatic Cancer Markers and various other signaling factors as cargo in cancer EVs
Project description:<p>Pancreatic cancer is an aggressive cancer with a poor prognosis. Metabolic abnormalities are one of the hallmarks of pancreatic cancer, and pancreatic cancer cells can adapt to biosynthesis, energy intake and redox needs through metabolic reprogramming to tolerate nutrient deficiency and hypoxic microenvironments. Pancreatic cancer cells can use glucose, amino acids and lipids as energy to maintain malignant growth. Moreover, they also metabolically interact with cells in the tumour microenvironment to change cell fate, promote tumour progression and even affect immune responses. Importantly, metabolic changes at the body level deserve more attention. Basic research and clinical trials based on targeted metabolic therapy or in combination with other treatments are in full swing. A more comprehensive and in-depth understanding of the metabolic regulation of pancreatic cancer cells will not only enrich the understanding of the mechanisms of disease progression but also provide inspiration for new diagnostic and therapeutic approaches.</p>